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Network slicing is the key to enable virtualized resource sharing among vertical industries in the era of 5G communication. Efficient resource allocation is of vital importance to realize network slicing in real-world business scenarios. To deal with the high algorithm complexity, privacy leakage, and unrealistic offline setting of current network slicing algorithms, in this paper we propose a fully decentralized and low-complexity online algorithm, DPoS, for multi-resource slicing. We first formulate the problem as a global social welfare maximization problem. Next, we design the online algorithm DPoS based on the primal-dual approach and posted price mechanism. In DPoS, each tenant is incentivized to make its own decision based on its true preferences without disclosing any private information to the mobile virtual network operator and other tenants. We provide a rigorous theoretical analysis to show that DPoS has the optimal competitive ratio when the cost function of each resource is linear. Extensive simulation experiments are conducted to evaluate the performance of DPoS. The results show that DPoS can not only achieve close-to-offline-optimal performance, but also have low algorithmic overheads. [ABSTRACT FROM AUTHOR]

The online retail market shows a sharp increase in traffic during holiday sales. The ability to distinguish customers who will likely purchase is critical for provisioning traffic and for providing cost-effective promotions. This paper uniquely studies the browsing and purchasing behaviors of online shoppers during a yearly sale event in China, the world’s largest online marketplace. Based on 31 million action logs gathered from wide residential areas, we characterize the steps leading to purchases and determine their precursors. We investigate the effect of time (e.g., date, time of date), environment (e.g., platform, viewed category), and action (e.g., session time, clicks, sequence) on purchases. Action cues from shopping behaviors can be used for early detection. While most shoppers start with strong intentions to purchase, yet the moment of ordering comes rather impulsively within 30 seconds to several minutes of browsing. The predictive accuracy reaches as a high AUC of 0.924. The findings in this paper provide an understanding of traffic during mega sale events that can help online shops plan and provide a better user experience for upcoming shopping festivals. [ABSTRACT FROM AUTHOR]

Future communication networksrequire higher bandwidth, greater coverage, and better throughput. The Space-Air-Ground Integrated Network (SAGIN) has the advantage of wide-area coverage and can cover global communications. It can meet the needs of network resources for maritime activities and remote mountainous areas, which is of great significance to the realization of a new generation of communications networks. With the development of the network and the emergence of delay-sensitive applications such as the Internet of Things, improving the delay performance of the system has received extensive attention. SAGIN involves multiple networks and is more complex than other networks. If there is no reasonable management between different networks, it is easy to lead to difficult link deployment and high time delay. On the basis of research and customization of SFC technology, it can provide a wide range of services and other advantages. It has significant performance in application scenarios involving multi network integration. In order to solve the above problems, this paper studies the SAGIN architecture of SFC based on business types. A service function chain mapping method based on delays prediction is proposed. Calculate the delay of the deployment path and select the path with the lowest delay as the SFC mapping path. The service model is constructed according to the mapping path, and the network slices are divided based on the service type. The simulation results show that the SFC mapping algorithm based on time delay prediction is compared with the traditional SFC mapping scheme. The algorithm does not affect other indicators, the CPU resource utilization rate is 27.8 $\%$ higher, and the link resource utilization rate is 22.7 $\%$ higher. The service acceptance rate increased by 21.5 $\%$ , the latency performance increased by 38.2 $\%$ , and the total resource consumption is reduced by 25.2 $\%$. [ABSTRACT FROM AUTHOR]

Smart contracts in Ethereum blockchain network are self-executable scripts used for managing tokenized assets and access rights between different entities on the blockchain network. They are significant addition to blockchain technology for achieving data transparency and reliability in maintaining the digital relationship between two or more entities. However, the smart contract is in its nascent stage and suffers from various limitations, including immutability and code secrecy. In this paper, we address the inability of smart contracts to access the on-chain data. The smart contract currently fetches the on-chain data by taking external oracle assistance, which can be compromised to influence the customers. To address the issue, we propose a decentralized mechanism for accessing the blockchain data directly from the smart contracts by indexing single or multiple parameters of a transaction in each block using the Merkle-Patricia Trie (MPT). The idea is to increase the data transparency of blockchain applications. The paper provides a sequential search methodology that can retrieve transactions from ${N}$ blocks, satisfying specific conditions, in ${O}$ (${N}$). The complexity is further reduced to ${O}$ (N/k) by partitioning the blockchain data into ${k}$ disjoint subsets to achieve parallelism in the search procedure. We experimentally verify the effectiveness of the proposed methodology with a case study on the Ethereum blockchain data. [ABSTRACT FROM AUTHOR]

Batch processing refers to an organization of work in which cases are synchronized such that they can be processed as a group. Prior research has studied batch processing mainly from a deductive angle, trying to identify optimal rules for composing batches. As a consequence, we lack methodological support to investigate according to which rules human resources build batches in work settings where batching rules are not strictly enforced. In this paper, we address this research gap by developing a technique to inductively mine batch activation rules from process execution data. The obtained batch activation rules can be used for various purposes, including to explicate the real-life batching behavior of human resources; to determine the compliance between the prescribed and actual batching behavior; or to investigate the influence of alternative batching behavior on service levels. The evaluation of our technique using both synthetic and real-world data demonstrates its effectiveness. With this work we complement prescriptive research on batch processing with a descriptive technique that is empirically grounded in process execution data. [ABSTRACT FROM AUTHOR]

In prior parts of this series, I analyzed the 1) number of all issued patents and issued computer architecture patents; 2) the prosecution time and effective patent term; 3) the number of claims, breakdown of independent and dependent claims, and effect that excess claim fees had on the numbers of total and independent claims; and 4) the type of claims (apparatus, method, or Beauregard), and effect that the Supreme Court's decision in Alice v. CLS Bank had on the number of independent and dependent method claims, for patents that were issued to 18 leading computer architecture companies and filed between 1996 and 2020. This article examines the 1) number of "backward" citations to previously issued/published U.S. patents and U.S. patent publications, foreign patents, and Other References and 2) the number of "forward" citations to a company's patent by another U.S. patent or U.S. patent publication. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc network (VANET) is an emerging aspect of the 5G vertical application. Network function virtualization (NFV) is the key enabling technology of 5G and beyond 5G (B5G) networks. In NFV-enabled vehicular and 5G networks, all underlying nodes (e.g. vehicular, edge, core) can be completely virtualized and easy to be managed and allocated. Network service providers can implement each dynamically requested virtual network service (VNS), having arbitrary topology and customized resource demands, on top of the NFV-enabled networks. However, network elements (e.g. nodes and links) may come into failures accidentally. Consequently, it will lead to the performance degradation of implemented VNSs that run on top of the failed network elements. It is vital to guarantee the survivable services even though the network elements fail accidentally. Therefore, we propose the dynamic virtual resource allocation mechanism in this paper. Firstly, we introduce the business model and formulate the dynamic virtual resource allocation in NFV-enabled networks. Secondly, we detail all modules of our proposed mechanism. Especially, the initial resource allocation and re-allocation modules of achieving the survivable network services are detailed. Finally, we execute the comprehensive simulations by comparing with the typical virtual resource allocation mechanisms. The simulation results are discussed so as to highlight the merits of the proposed mechanism. [ABSTRACT FROM AUTHOR]

So-called spatial web queries retrieve web content representing points of interest, such that the points of interest have descriptions that are relevant to query keywords and are located close to a query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, the top- $k$ k spatial textual cluster retrieval ($k$ k -STC) query that returns the top- $k$ k clusters that (i) are located close to a query location, (ii) contain objects that are relevant with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a DBSCAN-based approach and an OPTICS-based approach that rely on on-line density-based clustering and that exploit early stop conditions. Empirical studies on real data sets offer evidence that the paper's proposals can find good quality clusters and are capable of excellent performance. [ABSTRACT FROM AUTHOR]

In recent years, an emerging stream of research suggests that firms should use technological innovation and business model innovation harmoniously to maximize firm performance. In order to enrich the scholarly conversation on the fit between technological innovation and business model innovation, drawing insights from the dynamic capabilities perspective, this paper examines the fit between value proposition innovation and technological innovation (exploitative versus explorative) for the performance of startups in the digital environment. Based on on-site survey data of 285 digital startups in one of the world's largest digital economies, we find that explorative innovation strengthens the positive impact of value proposition innovation on the performance of startups, whereas exploitative innovation weakens this positive effect. Furthermore, these moderating effects are magnified in a highly uncertain demand environment. These findings inject fresh insights into existing scholarly conversation on the fit between technological innovation and business model innovation by offering a dynamic capabilities perspective and by extending it to an increasingly digital business environment. [ABSTRACT FROM AUTHOR]

Online social networks have become major battlegrounds for political campaigns, viral marketing, and the dissemination of news. As a consequence, “bad actors” are increasingly exploiting these platforms, which is a key challenge for their administrators, businesses and society in general. The spread of fake news is a classical example of the abuse of social networks by these bad actors. While some have advocated for stricter policies to control the spread of misinformation in social networks, this often happens in detriment of their democratic and organic structure. In this paper, we aim to limit the influence of a target group in a social network via the removal of a few users/links. We formulate the influence limitation problem in a data-driven fashion, by taking into account past propagation traces. More specifically, our algorithms find critical edges to be removed in order to decrease the influence of a target group based on past data. The idea is to control the diffusion processes while minimizing the amount of disturbance in the network structure. Moreover, we consider two types of constraints over edge removals, a budget constraint and also a, more general, set of matroid constraints. These problems lead to interesting challenges in terms of algorithm design. For instance, we are able to show that influence limitation is APX-hard and propose deterministic and probabilistic approximation algorithms for the budgeted and the matroid version of the problem, respectively. Experiments show that the proposed approaches outperform several baselines. [ABSTRACT FROM AUTHOR]

Cryptocurrency mining device (CMD) is a plug-and-play device installed by any low-voltage customer to convert electrical energy to digital money. Recently, the significant increase in the price of digital currencies has persuaded residential customers in many countries to employ CMDs to obtain quick and easy profit. However, these rapidly evolving loads can cause a substantial increase in the coincident peak load of distribution networks. Therefore, it can threaten the safe and reliable operation of the network. A straightforward way to restrict the penetration rate of these home-based miners is to increase the electricity price such that this business becomes unprofitable; however, this solution affects the national regulations and is not practical in many countries. This paper proposes a practical electricity pricing strategy to manage the operation of cryptocurrency mining loads appropriately. This pricing strategy can change the miner’ threat to an opportunity to sell more electrical energy during the off-peak hours to these loads and to maximize the profit of the utility. The proposed strategy is studied using the load profile dataset obtained from the measurements of Tehran Electricity Distribution Company, and its effectiveness is demonstrated. [ABSTRACT FROM AUTHOR]

As one representative of the emerging on-demand transport services, the on-demand food delivery (OFD) has penetrated into daily life. Due to its intrinsic complexities, the OFD has attracted the interest of a growing number of logistics researchers. This paper aims at optimizing the OFD process and addresses an OFD problem (OFDP). To overcome the dynamic and large-scale complexity, we abstract the OFDP into a static generalized assignment problem with a rolling horizon strategy. To meet the demand on high service quality and limited computation time, we propose an offline-optimization for online-operation framework based on imitation learning. Under this framework, an imitation learning-enhanced iterated matching algorithm (ILIMA) is proposed, which consists of three basic components: an iterated matching heuristic (IMH) to fast generate solutions, an expert to provide expertise, and a machine learning (ML) model to assist the decision-making process in IMH by mimicking the expert. In the offline-optimization phase, the ML model mines knowledge from the high-quality solutions optimized by the expert; in the online-operation phase, the IMH embedded with the well-trained ML model is deployed online to make decisions in a real OFD scenario. Offline simulation experiments are carried out on real historical data, which validate the superiority of ILIMA compared with existing methods. Moreover, rigorous online A/B tests are conducted on the scheduling system of Meituan, which demonstrates the practical value of ILIMA to improve customer satisfaction and delivery efficiency. [ABSTRACT FROM AUTHOR]

In the era of Industrial Internet of Things (IIoT) and Industry 4.0, an immense volume of heterogeneous network devices will coexist and contend for shared network resources, in order to satisfy the very challenging IIoT applications, requiring ultra-reliable and ultra-low latency communications. Although novel key enablers, such as Network Slicing, Software Defined Networking (SDN) and Network Function Virtualization (NFV) have already offered significant advantages towards more efficient and flexible network and resource management approaches, the particular characteristics of IIoT applications pose additional burdens, mainly due to the complex wireless environments, high number of heterogeneous network devices, sensors, user equipments (UEs), etc., which may stochastically demand and contend for the - often scarce - computing and communication resources of industrial environments. To this end, this paper introduces PRIMATE, a novel, Artificial Intelligence (AI)-driven framework for the profiling of the networking behavior of such UEs, devices, users and things, which is able to operate in conjunction with already standardized or forthcoming, AI-based network resource management processes towards further gains. The novelty and potential of the proposed work lies on the fact that instead of attempting to either predict raw network metrics in a reactive manner, or predict the behavior of specific network entities/devices in an isolated manner, a big data-driven classification approach is introduced, which models the behavior of any network device/user from both a macroscopic, as well as service-specific perspective. The extended evaluation at the last part of this work shows the validity and viability of the proposed framework. [ABSTRACT FROM AUTHOR]

We argue in this paper that the role of the cloud should be reshaped from being a passive virtual market to become an active platform for monetizing data. The objective is to enable the cloud to be an active platform that can help data providers reach a wider set of data consumers. This will allow these consumers to be exposed to a larger variety of data that benefits data analytic applications. To achieve this vision, we propose a novel game theoretical model, which consists of a mix of cooperative and competitive strategies. The players of the game are the data providers, cloud platform, and cloud users. The strategies of the players are modeled using the two-sided market theory that takes into consideration the network effects (externalities) among the players. Simulations conducted using Amazon and google clustered data show that the proposed model improves the total surplus of involved parties in terms of cloud resources provision and monetary profits compared to the current merchant model. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic pricing and revenue-driven service federation strategy based on a Deep Q-Network (DQN) to instantly and automatically decide federation across different service provider domains, each introduces dynamic service prices offering to its customers and towards other domains. A dynamic pricing model is considered in this work based on the analysis of real pricing data collected from public cloud provider, and upon this a dynamic arrival process as a result of the price changes is proposed for formulating the service federation problem as a Markov Decision Problem (MDP). In this work, several reinforcement learning algorithms are developed to solve the problem, and the presented results show that the DQN method reached 90% of the optimal revenue and outperformed existing state-of-the-art strategies, and it can learn the federation pricing dynamics to make optimum federation decisions according to price changes. [ABSTRACT FROM AUTHOR]

E-commerce platforms incorporate reputation systems that allow customers to rate suppliers following financial transactions. Existing reputation systems cannot defend the centralized server against arbitrarily tampering with the supplier’s reputation. Furthermore, they do not offer reputation access across platforms. Rates are faced with privacy leakages because rating activities are correlated with privacy (e.g., identity and rating). Meanwhile, raters could be malicious and initiate multiple rating attacks and abnormal rating attacks. Determining how to address these issues have both research and practical value. In this paper, we propose a blockchain-based privacy-preserving reputation system for e-commerce platforms named RepChain; our system allows cross-platform reputation access and anonymous and private ratings. Using RepChain, all e-commerce platforms collaborate and share users’ reputations by co-constructing a consortium blockchain and modeling the rating process as a finite state machine. In particular, we facilitate one-show anonymous credentials constructed from two-move blind signatures to protect customers’ identities and resist multiple rating attacks, leverage zero-knowledge range proof to verify the correctness of ratings and defend against abnormal rating attacks, design a secure sum computation protocol among nodes to update reputations, and verify ratings via batch processing and consensus hashes. Finally, we demonstrate the security and privacy of RepChain via a formal analysis and evaluate its performance based on Ethereum test network. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing, more and more enterprises would like to upload and store their data in the public cloud. When the parts of the business of an enterprise are purchased by another enterprise, the corresponding data will be transferred to the acquiring enterprise. For the usual case, how to outsource the computation cost of data transfer to the cloud? How to ensure the remote purchased data integrity? Thus, it is important to study provable data possession with outsourced data transfer (DT-PDP). In this paper, for the first time, we propose the novel concept: DT-PDP. By taking use of DT-PDP, the following three security requirements can be satisfied: (1) the other un-purchased data security of acquired enterprise can be ensured; (2) the purchased data integrity and privacy can be ensured; (3) the data transferability’s computation can be outsourced to the public cloud servers. For the security concept of DT-PDP, we give its motivation, system model and security model. Then, we design a concrete DT-PDP scheme based on the bilinear pairings. At last, we analyze the security, efficiency and flexibility of the concrete DT-PDP scheme. It shows that our scheme is provably secure and efficient. [ABSTRACT FROM AUTHOR]

Social data outsourcing is an emerging paradigm for effective and efficient access to the social data. In such a system, a third-party Social Data Provider (SDP) purchases social network datasets from Online Social Network (OSN) operators and then resells them to data consumers who can be any individuals or entities desiring social data through query interfaces. The SDP cannot be fully trusted and may return forged or incomplete query results to data consumers for various reasons, e.g., in favor of the businesses willing to pay. In this paper, we initiate the study on verifiable query processing over outsourced social graph whereby a data consumer can verify both the integrity and completeness of any query result returned by an untrusted SDP. We propose three schemes for single-attribute queries and another scheme for multi-attribute queries over outsourced social data. The four schemes all require the OSN provider to generate some cryptographic auxiliary information, based on which the SDP can construct a verification object to allow the data consumer to verify the integrity and completeness of the query result. They, however, differ in how the auxiliary information is generated and how the verification object is constructed and verified. Detailed analysis and extensive experiments using a real Twitter dataset confirm the efficacy and efficiency of the proposed schemes. [ABSTRACT FROM AUTHOR]

In this paper, we determine the risk mitigation process inherent in managing a portfolio of technologies diverse in both their readiness for infusion and the nature of the performing organization, focusing on the so-called “valley of death” in which the technology's principles have been proven but prototypes have yet to be developed. Using the Technology Readiness Levels (TRLs) of projects funded by the National Aeronautics and Space Administration Small Business Innovation Research program, a two-stage competitive process, we find that the result of selection of the first round is a tendency toward larger companies. In the second round of funding, technology maturity is a stronger determinant of selection and company headcount is no longer a statistically significant driver. This combination allows the program to manage risk and deliver real technical advancement from even the smallest companies. We find that technologies typically advance from TRL 2, concept formulation, at the program's outset to roughly TRL 5, component validation, at the program's conclusion; these outcomes precede economic benefits from the subsidy. These findings illuminate a mechanism to address risk as well as demonstrating the technical outcomes of a managed early-stage technology program. [ABSTRACT FROM AUTHOR]

The adoption of virtualization and cloud computing technologies have revolutionized how services and applications can be developed, deployed, and operated to achieve better elasticity, flexibility, and scalability. Multiple stakeholders can be involved for providing online services; each of them plays one or more roles (i.e., service operator, application vendor, and infrastructure provider) to create a customized operating model based on the business requirements. The operating model changes from one business to another, and it may even change at different stages of the same business. A trusted relationship among stakeholders for secure information exchange is the key to enable such flexibility. However, traditional usage compliance methods (e.g., in-person audit, dynamic licensing, and subscription) lack explicit trust among involved parties and the flexibility and scalability to support dynamic sizing of services and applications with low overhead. In this work, we argue the need for a new trust framework to manage application usage rights and propose Metered Boot to provide trusted, capacity/usage-based usage rights management for services and applications deployed in virtualized environments. Metered Boot decouples application workload instantiation for service operators, usage rights governance for application vendors, and resource provisioning for infrastructure providers. We leverage cryptoprocessors (e.g., Trusted Platform Module (TPM)) on commodity servers to generate trusted proofs which are managed by efficient cryptographic construction, Merkle hash tree, for usage rights compliance. We integrated our framework with OpenStack and demonstrate that Metered Boot is able to achieve high scalability and low overhead for instantiating virtual network functions (VNFs). [ABSTRACT FROM AUTHOR]

Event sequence data is increasingly available in various application domains, such as business process management, software engineering, or medical pathways. Processes in these domains are typically represented as process diagrams or flow charts. So far, various techniques have been developed for automatically generating such diagrams from event sequence data. An open challenge is the visual analysis of drift phenomena when processes change over time. In this article, we address this research gap. Our contribution is a system for fine-granular process drift detection and corresponding visualizations for event logs of executed business processes. We evaluated our system both on synthetic and real-world data. On synthetic logs, we achieved an average F-score of 0.96 and outperformed all the state-of-the-art methods. On real-world logs, we identified all types of process drifts in a comprehensive manner. Finally, we conducted a user study highlighting that our visualizations are easy to use and useful as perceived by process mining experts. In this way, our work contributes to research on process mining, event sequence analysis, and visualization of temporal data. [ABSTRACT FROM AUTHOR]

Adaptive workflow management systems allow workflows to be changed in both the modeling and runtime stages, resulting in many workflow variants. Identifying a minimum sequence of high-level changes between two workflows represents a fundamental yet critical issue. The state-of-the-art approach utilizes digital logic to seek the optimal solution; however, this approach may face difficulties when advanced workflow patterns (e.g., loops) are involved, and it does not scale well. To address this problem, we first propose a naive approach that applies all valid changes to one workflow until the other workflow is found. Then, the approach is optimized from two aspects. First, we present advanced heuristics that significantly reduce the search space without pruning the optimal solution. Second, we employ the A $^\ast$ * search algorithm to direct the search procedure. Because the heuristic function used in the A $^\ast$ * algorithm is problem specific, we devise a consistent heuristic function to approximate the edit distance between two workflows, thereby accelerating the search. We implement our approach in a prototype tool and conduct extensive experiments on two data sets to evaluate its effectiveness and efficiency. The experimental results demonstrate that our approach outperforms the state of the art in terms of both application scope and scalability. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are at the intersection of transportation systems and energy systems. The EV batteries, an increasingly prominent type of energy resource, are largely underutilized. We propose a new business model that monetizes underutilized EV batteries as mobile energy storage to significantly reduce the demand charge portion of many commercial and industrial users’ electricity bills. This business requires minimal hardware to enable discharging the batteries of electric vehicles and a sharing platform that matches EVs to commercial electricity users in real time. Using real meter data, we show that a large number of users can be served by a small number of EVs in the proposed business model. Cost-revenue analysis based on a real electric tariff suggests that the demand charge saving covers the capital costs of needed hardware and the compensation for EV drivers to provide the service. [ABSTRACT FROM AUTHOR]

Separating decision modelling from the processes modelling concern recently gained significant support in literature, as incorporating both concerns into a single model impairs the scalability, maintainability, flexibility, and understandability of both processes and decisions. Most notably the introduction of the Decision Model and Notation (DMN) standard by the Object Management Group provides a suitable solution for externalising decisions from processes and automating decision enactments for processes. This article introduces a systematic way of tackling the separation of the decision modelling concern from process modelling by providing a Decision as a Service (DaaS) layered Service-Oriented Architecture (SOA) which approaches decisions as automated and externalised services that processes need to invoke on demand to obtain the decision outcome. The DaaS mechanism is elucidated by a formalisation of DMN constructs and the relevant layer elements. Furthermore, DaaS is evaluated against the fundamental characteristics of the SOA paradigm, proving its contribution in terms of abstraction, reusability, loose coupling, and other pertinent SOA principles. Additionally, the benefits of the DaaS design on process-decision modelling and mining are discussed. Finally, the DaaS design is illustrated on a real-life event log of a bank loan application and approval process, and the SOA maturity of DaaS is assessed. [ABSTRACT FROM AUTHOR]

Research questions: 1. How can we address issues of diversity, equity, and inclusion (DEI) in our business and technical communication service courses? 2. How can we help prepare future engineers, technical professionals, and managers to create more inclusive and equitable workplaces?. Situating the case: The social justice literature in technical and professional communication (TPC) has focused on a variety of areas, including research methods, user experience, and expanding what can and should be identified as TPC. Emerging research has turned toward pedagogy as an interventional strategy for educating on issues of racial justice and inclusion. About the case: This teaching case presents the transformation of a TPC service course to specifically address issues of diversity, equity, and inclusion (DEI). In response to the racial injustice documented during the summer of 2020, I developed a sequence of assignments that asked students to research and apply DEI initiatives. The assignment was to research and write a short report on DEI approaches in the workplace, followed by a larger team-based project in which students worked with the local city council to enact possible DEI initiatives in the broader community. Methods: The case was studied through the author's experience and the analysis of data obtained from surveys with class participants and other instructors who incorporated the assignments in their courses. Results/discussion: Students were able to learn more about how to address bias, inclusion, and social justice in a business environment, but also demonstrated some implicit resistance to direct attention to racial injustice. The case study humanizes and brings home issues of diversity, equity, and inclusion for students who might otherwise consider them only in the abstract. [ABSTRACT FROM AUTHOR]

The correctness of workflow systems is dependent on not only their control-flows but also their data-flows. WFD-nets (Workflow net with data) can model both control-flow and data-flow of workflow systems, and are used to analyze/verify their correctness. The existing methods of analyzing/verifying WFD-nets are based on their reachability graphs. However, the reachability-graph-based methods have two flaws: state space explosion and pseudo states, since the guard functions are not handled perfectly. Note that every transition of a WFD-net is labeled by a guard function, which is a Boolean expression w.r.t. data and can determine the progress of control-flow. In order to solve these problems, we propose a guard-driven reachability graph that can both alleviate the state space explosion problem and avoid pseudo states. We describe the related algorithms and develop a tool. A group of experiments illustrate the advantage and effectiveness of our approach, and an example of property loan shows its usefulness. [ABSTRACT FROM AUTHOR]

As an emerging social dynamic system, geo-social network can be used to facilitate viral marketing through the wide spread of targeted advertising. However, unlike traditional influence spread problem, the heterogeneous spatial distribution has to incorporated into geo-social network environment. Moreover, from the perspective of business managers, it is indispensable to balance the tradeoff between the objective of influence spread maximization and objective of promotion cost minimization. Therefore, these two goals need to be seamlessly combined and optimized jointly. In this paper, considering the requirements of real-world applications, we develop a multiobjective optimization-based influence spread framework for geo-social networks, revealing the full view of Pareto-optimal solutions for decision makers. Based on the reverse influence sampling (RIS) model, we propose a similarity matching-based RIS sampling method to accommodate diverse users, and then transform our original problem into a weighted coverage problem. Subsequently, to solve this problem, we propose a greedy-based incrementally approximation approach and heuristic-based particle swarm optimization approach. Extensive experiments on two real-world geo-social networks clearly validate the effectiveness and efficiency of our proposed approaches. [ABSTRACT FROM AUTHOR]

Finding influential nodes reshuffles the very notion of linear paths in business processes and replaces it with networks of business value within a smart enterprise system. There are many existing algorithms for identifying influential nodes with certain limitations for applying in large-scale networks. In this article, we propose a community structure with integrated features ranking (CIFR) algorithm to find influential nodes in the network. First, we use the community detection algorithm to find communities in the system, and then we rank the nodes of network based on three factors, namely local ranking, gateway ranking, and community ranking, collectively termed as integrated features. Our algorithm intends to select influential nodes, which are both globally and locally optimal, leading to overall high information propagation. We perform the experimental results on total eight networks using various evaluation parameters. The obtained results validate superior performance against contemporary algorithms adding value to smart enterprises. [ABSTRACT FROM AUTHOR]

Nowadays, much research leverages the clustering to mine commercial patterns from data in enterprise systems. However, previous methods cannot fully consider local structures and global topology of data, which may cause the degradation of clustering performance. To address the challenges, a deep multimodal adversarial cycle-consistent network (DMACCN) is proposed to mine intrinsic patterns of data, which can capture the local structures from instance reconstructions and the global topology from adversarial games. Specifically, DMACCN is designed as an adversarial encoding-decoding architecture composed of the modality specific-encoder, the modality-common fusion network, the cycle-consistent modality-specific generator, and the modality-fusion discriminator, which can fully fuse complementary information of data. Then, an adversarial cycle-consistent loss is devised to guide the clustering pattern mining from complementary information of data, which can align semantics between modalities and capture clustering structures of instances. The two components collaborate in a seamless manner to capture accurate commercial patterns. Finally, extensive experimental results on four datasets show DMACCN greatly outperforms the comparison methods. [ABSTRACT FROM AUTHOR]

Manufacturing companies are currently undergoing a digitalization transformation in which digitally enabled, new, and innovative advanced service offerings are being launched. These so-called “digital services” represent a shift in the business logic of manufacturing firms, from up-front product sales to advanced service contracts. This business model shift has profound implications for cost structures, risk management, and revenue streams, providing manufacturing companies with the key challenge of rethinking how to capture value. Using a multiple case study of 11 companies, the purpose of this article is to enhance knowledge on how to design new revenue models for digital services. Results reveal a revenue model design framework of key phases and activities that carries implications for the emerging literature on digital servitization, as well as the business model innovation literature. The findings reveal a highly customer-centric, iterative, and agile process where close collaboration with key customers during the early stages guides the framing of revenue models for digital services. For practitioners, it provides hands-on advice on how to implement the design, development, and scaling processes for revenue models in the context of new digital services. [ABSTRACT FROM AUTHOR]

Semiconductor intellectual property (SIP) represents a crucial component of integrated circuit design activities. However, many small SIP firms fail to create value and adopt a viable business model. This article aims to investigate the value creation mechanism within the SIP business by drawing on the business model and dynamic capabilities views of the firm. First, the authors classify the forms and types of SIP from a technology and market perspective. Analyzing ten leading SIP firms, three SIP business models based on resources, competencies, and strategies are then identified. Dynamic capabilities are then linked to the selection and implementation of classified business models. Findings of the study suggest that existing SIP business models primarily favor large vendors. Keystone business model is proposed as a possible successful business model for small vendors. Consequently, this article provides insights and opportunities for leveraging the SIP ecosystem and business model innovation for small firms in the industry. [ABSTRACT FROM AUTHOR]

Most existing studies on next point-of-interest (POI) recommendation assume that users deliver certain check-ins over individual POIs. In reality, we typically obtain uncertain check-ins due to the presence of collective POIs, which are gathering places of multiple individual POIs (e.g., shopping malls). On one hand, such uncertain check-ins over collective POIs hinder more accurate next POI recommendation for users due to the transition vanishing issue; on the other hand, the presence of collective POIs poses the challenge for businesses to select which collective POIs to locate in due to complicated competition and cooperation relations between businesses. As such, these collective POIs bring an unprecedented opportunity and necessity on recommendation for both users and businesses. Therefore, we propose novel solutions of location service beneficial for users-businesses. For users, we propose the STSP equipped with category- and location-aware encoders, to deliver more accurate next POI prediction by fusing rich context features. Regarding businesses, we explore their competition and cooperation relations from check-in records, based on which we derive the living environment (LE) of a business. Insight on site selection for businesses is provided by exploiting the LE, aiming to bring in more profits. Extensive empirical studies demonstrate the efficiency of our solutions. [ABSTRACT FROM AUTHOR]

Fake check scam is one of the most common attacks used to commit fraud against consumers. This fraud is particularly costly for victims because they generally lose thousands of dollars as well as being exposed to judicial proceedings. Currently, there is no existing solution to authenticate checks and detect fake ones instantly. Instead, banks must wait for a period of more than 48 h to detect the scam. In this context, we propose a blockchain-based scheme to authenticate checks and detect fake check scams. Moreover, our approach allows the revocation of used checks. More precisely, our approach helps the banks to share information about provided checks and used ones, without exposing the banks’ customers’ personal data. We demonstrate a proof of concept of our proposed approach using Namecoin and Hyperledger blockchain technologies. [ABSTRACT FROM AUTHOR]

This article develops and tests a primary conceptual model of the direct effects of business environment on supply chain integration (SCI) and an alternative moderation model to better understand the role of the environment in developing integration and financial performance improvement. Structural equation modeling and moderated regression are used to analyze the survey data collected from manufacturing firms in China. The results reveal significant positive relationships between environmental dynamism and all three dimensions of SCI, and between environmental hostility and internal integration. The results also indicate that internal integration fully mediates the relationships between environmental hostility and both customer and supplier integration. Supplier integration is significantly and positively associated with financial performance, whereas internal and customer integration are not significantly associated with performance. Importantly, through the test of an alternative model, environmental hostility and environmental dynamism are shown to affect SCI directly, rather than moderating the SCI-performance relationship. This article provides useful guidelines for managers to develop integrated supply chains to improve the financial performance under dynamic and hostile environments. [ABSTRACT FROM AUTHOR]

Consensus reaching process (CRP) is important and present in a wide range of application areas. In practical CRP, the managers (e.g., enterprise) often hire some informed individuals (e.g., persuaders) to promote the efficiency of consensus reaching. This article proposes a CRP with minimum cost of informed individuals and time constraint in large-scale group decision-making (LSGDM) with bounded confidence effects. The consensus model with bounded confidence effects (CBC model) is formulated. Then, desirable properties of the CBC model are discussed to facilitate its resolution. Next, an extended particle swarm optimization algorithm is designed to solve the CBC model. Finally, a numerical analysis, a comparison analysis, and a simulation analysis are provided to illustrate the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Process mining aims at discovering behavioral knowledge of business processes from their event logs, which has received an increasing attention in the era of cloud computing and big data. Surprisingly, to date, discovering structural errors (e.g., deadlocks and lack of synchronization) from event logs has not been considered in state-of-the-art process mining techniques. Moreover, existing process discovery approaches cannot be directly applied to event logs of processes with structural errors due to erroneous event occurrences caused by unsynchronized activities. To address this problem, we first preprocess the event log to obtain two separate event logs that are used to discover deadlocks and lack of synchronization, respectively. Erroneous event occurrences caused by unsynchronized activities are discarded in the two processed event logs, from which our error mining algorithms can discover all process fragments involving structural errors, without the need to obtain the overall process first. We implement our approach in a ProM plugin and evaluate it on event logs of real-life business processes, the results of which demonstrate that our approach can effectively and efficiently discover deadlocks and lack of synchronization if event logs contain sufficient event sequences. [ABSTRACT FROM AUTHOR]

The challenges of the COVID-19 pandemic have readjusted government services available to citizens and the way of doing business. Digital transformation involves using digital technologies to remake public services and ensure successful business operations. Therefore, the country's economy and society must be ready to make the most of digitalization. The quantitative and qualitative methods in this study are used to analyze countries’ economic transformation under the COVID-19 pandemic. The findings suggest that digital transformation is quickly becoming a priority for many sectors in Ukraine. According to the developed exponential model, the rate of development of transformation processes in Ukraine in 2021–2025 will advance. At the same time, the main problems of digitalization in Ukraine are legal complexity, insufficient funding, and lack of digital literacy and skills among the active population. The findings presented within this article can be found useful by managers of the real economy sector deciding on the need for the digital transformation of an enterprise. Likewise, the conclusions made are seen as beneficial for academic community in general since they open up new opportunities in terms of digital transformation research. [ABSTRACT FROM AUTHOR]

Fifty-two years ago, Milton Friedman proclaimed that the “Social Responsibility of Business is to Increase its Profits.” The corporate executive, he said, owed one thing to one group: profit to his “employers,” the shareholders of the firm. And with this rallying cry, he summoned forth a near 30-year movement (involving scholars, regulators, political parties, and others) to force the American corporation to conform to his vision, by doing everything from increasing shareholder control of the firm to linking CEO compensation with short-term share prices. [ABSTRACT FROM AUTHOR]

The massive amounts of monitoring data in network applications bring an urgent need for intelligent operation in large distributed systems. The key problem is precisely detecting anomalies in multivariate time series (MTS) monitoring metrics with the awareness of different application scenarios. Unsupervised MTS anomaly detection methods aim at detecting data anomalies from historical MTS without considering the out-of-band information (including user feedback and background information like code deployment status), which leads to poor performance in practice. To take advantage of the out-of-band information, we propose ACVAE, an MTS anomaly detection algorithm through active learning and contrast VAE-based detection models, which simultaneously learns MTS data’s normal and anomalous patterns for anomaly detection. We also use a learnable prior to capture system status from the background information. Moreover, we propose a query model for VAE-based methods, which can learn to query labels of the most useful instances to train the detection model. We evaluate our algorithm on three different monitoring situations in eBay’s search back-end systems. ACVAE achieves a range F1 score of 0.68~0.96 with only 3% labels, significantly outperforming the best competing methods by 0.18~0.50, and even better than a supervised ensemble method designed by domain experts in eBay. [ABSTRACT FROM AUTHOR]

The success of early-stage start-ups is strongly dependent on founders’ education. Yet, while scholars maintain that investors should consider founders' education an important signal of start-ups' prospects, mixed empirical findings suggest that more education does not always trigger positive investor reactions. Scholars have thus urged the development of new theoretical insights that may explain why and when education is beneficial to start-ups' emergence. Based on cognitive and social psychology literature, in this article, we evidence that different configurations of founders' education affect the ability of founding teams to obtain funding. The empirical support that we obtained from a sample of 1078 start-ups helps reconcile the contradictory findings of past research through a joint examination of two dimensions of founders' education that were previously only investigated separately—the level of education and the heterogeneity of educational backgrounds. Our focus on the group dynamics founding teams advances our understanding of the contribution of founders’ education onto early-stage start-ups' fund raising. In greater detail, educational level and educational heterogeneity positively affect the amount of funds raised, but their joint presence negatively moderates such a relationship. In doing so, we also provide start-uppers and policymakers with important information regarding how cofounders should group together when launching a start-up. [ABSTRACT FROM AUTHOR]

This article investigates the stock market reaction to appointments of newly created chief digital or data officer (CDO) positions. The analysis is based on a sample of 112 CDO appointment announcements by publicly traded companies listed in the US stock market from 2004 to 2017. We ground our arguments in signaling theory along with the institutional entrepreneurship and synergy and redundancy perspective to understand the factors that could influence the market reaction to CDO appointments. Although the results show that the stock market reacts neutrally to announcements of newly created CDO positions, the market does react positively under certain conditions. The market reacts positively when appointing firms exhibit high growth prospects. The article supports the redundancy perspective, and the results show that the market reacts positively when a potentially conflicting and overlapping role such as chief information officer (CIO) is absent in appointing firms. Our analysis also shows that when CIO is absent, the market reacts more positively to outsider CDO relative to insider CDO, thereby indicating the interaction between redundancy and institutional entrepreneurship perspectives. [ABSTRACT FROM AUTHOR]

The main sources of technological development are from abroad rather than domestic in many countries. The global networks for knowledge and technology flows are becoming sophisticated. This article examines how a country's embeddedness in the global knowledge spillover networks, particularly trade and FDI, is related to national innovation performance. Network closure and structural hole, the main arguments in network theory literature, represent the main objectives of our network analysis. The results are subsequently used to assess how these network characteristics are related to national innovation performance via panel negative binomial regression analysis. The findings show an overall tendency of a negative effect of network closure and a positive one of structural holes on a country's innovative outcome. In conclusion, this article not only highlights the significant role of network characteristics in national innovation performance, but also provides implications on how to leverage such network resources to improve national innovation systems. [ABSTRACT FROM AUTHOR]

Due to energy efficiency and near real-time finality settlement, permissioned blockchain is mainly used in blockchain-based Internet of Things (BIoT) services. This blockchain requires validators to reach the consensuses. Currently, the validators are chosen based on generic attributes of trusted nodes capriciously. However, service users have no mechanism to provide their requirements in assessing validators. Existing works are biased from users’ perspectives, which risks collusion attacks due to improperly selected validators. This article aims to solve this by considering users’ expectations on services. A user-oriented framework is proposed to evaluate the trust of BIoT services with the consideration of validator attributes. It provides an effective mechanism to express requirements. Specification language is adopted in our algorithm to perform compliance checking. The feasibility has been verified in a simulation. We found that the permissioned blockchain has also benefited from the proposed user-oriented selection of validators in terms of computing efficiency due to additional constraints. [ABSTRACT FROM AUTHOR]

The predictive business process monitoring is a family of online approaches to predict the unfolding of running traces based on the knowledge learned from historical event logs. In this article, we address the task of predicting the next trace activity from the completed events in a running trace. This is an important business capability as counting on accurate predictions of the future activities may allow companies to guarantee the higher utilization by acting proactively in anticipation. We propose a novel predictive process approach that couples multi-view learning and deep learning, in order to gain predictive accuracy by accounting for the variety of information possibly recorded in event logs. Experiments with various benchmark event logs prove the effectiveness of the proposed approach compared to several recent state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Blockchain, an emerging paradigm of secure and shareable computing, is a systematic integration of 1) chain structure for data verification and storage, 2) distributed consensus algorithms for generating and updating data, 3) cryptographic techniques for guaranteeing data transmission and access security, and 4) automated smart contracts for data programming and operations. However, the progress and promotion of Blockchain have been seriously impeded by various security issues in blockchain-based applications. Furthermore, previous research on blockchain security has been mostly technical, overlooking considerable business, organizational, and operational issues. To address this research gap from the perspective of information systems, we review blockchain security research in three levels, namely, the process level, the data level, and the infrastructure level, which we refer to as the PDI model of blockchain security. In this survey, we examine the state of blockchain security in the literature. Based on the insights obtained from this initial analysis, we then suggest future directions of research in blockchain security, shedding light on urgent business and industrial concerns in related computing disciplines. [ABSTRACT FROM AUTHOR]

Designing a value cocreation system in the manufacturing industry has seen significant interest in the past two decades. The importance of a value cocreation system has increased along with the social expectations related to increasing resource efficiency and the emergence of enablers such as digitalization. In this article, I take this opportunity to disseminate some findings to industry and managers. Specifically, a review of product/service systems (PSSs) where a manufacturer—taking responsibility for and control of larger ranges in a value network—integrates their products and services to cocreate and capture higher value. PSS design implications and how to implement PSS design are discussed. Insights into designing a PSS in manufacturing practice are highlighted largely based on collaborative experience with large global manufacturers. Strategic management of design activities to avoid design fixation and service design trap is key to the successful design of PSSs. [ABSTRACT FROM AUTHOR]

Organizations have been integrating sustainability issues into their systems. Employees are a core component of the human dimension of the organizational systems; however, there has been limited research on the factors of employee participation (EP) for organizational sustainability. This article is aimed at investigating the importance of EP factors and how they are related. A survey was conducted to investigate the importance and relationships of EP factors for sustainability, and 305 full responses were received. The responses were analyzed using Friedman tests, Kruskal–Wallis test, correlation analysis, and centrality measures. The results highlight that all factors are important for organizational sustainability, albeit some more than others. The correlation and centrality analyses showed that all factors are interrelated. This article provides insights into EP factors by 1) ranking of EP factors in organizations, 2) analyzing the interrelations and centrality of the EP factors, and 3) comparing the rankings, the interrelations, and centrality measures. This research contributes to organizational sustainability by focusing on the human dimensions through the EP factors and their interrelations. The EP factors must be recognized and integrated to implement sustainability more efficiently in organizations. No organization exists without its employees, and no organization can become sustainable without engaging them. [ABSTRACT FROM AUTHOR]

Social network group decision making (SNGDM) has emerged as a new decision tool to effectively model the social trust relationships among decision makers. The impact of the social trust relationships on assessments-modifications in the consensus reaching in the SNGDM is seldom considered. This study aims at addressing this issue. The main starting point is the assumption that a decision maker will not be willing to accept the assessments-modifications suggestions that significantly differ from his/her trusted decision makers’ assessments in a social trust network. Thus, this study proposes a social trust driven minimum adjustments consensus model (STDMACM) for SNGDM. Simultaneously, a social trust driven consensus maximum optimization model (STDCMOM) is proposed for maximizing the consensus level among decision makers under the above assumption. Based on both STDCMOM and STDMACM, an interactive consensus reaching process is presented, in which the assessments-modifications suggestions generated from the STDMACM are used, when the maximum consensus level obtained from STDCMOM is acceptable, as the references for guiding the consensus reaching; otherwise, assessments-modifications suggestions are generated from the designed STDCMOM. The validity of the social trust driven consensus reaching process with respect to its consensus convergence rate and consensus success ratio is verified with a simulation and comparison analysis. [ABSTRACT FROM AUTHOR]

Over the past few years, unmanned aerial vehicles (UAVs) and other aerial platforms have become central to several aspects of business and daily life. From remote delivery to acquiring landscape information to providing connectivity in emergency scenarios, drone features are proving to be highly beneficial in places where humans cannot reach or are unable to perform in a timely, efficient, or secure manner, thus paving the way for several disruptive new applications. [ABSTRACT FROM AUTHOR]

Business process similarity measures are of vital importance for process repository management applications, such as process query, process recommendation, and process clustering. Most existing approaches measure process similarity by relying on control-flow structures only. This article investigates the role of data in process similarity measure. To incorporate data-flow information into business process control flow, it proposes a data-aware workflow net (DWF-net) by extending the classical workflow net with data reading and writing semantics. Then, we introduce three types of similarity measures, i.e., data item set-based similarity, data operation set-based similarity, and data-aware behavior-based similarity, to quantify the similarity of data-aware business processes from different perspectives. Next, a methodology is introduced to help process analysts apply these three measures in a systematical way. Finally, we evaluate the effectiveness and applicability of the proposed similarity measures by a group of comparative experiments. Note to Practitioners—Business process similarity measures play an increasingly important role in business process repository management. This work addresses the role of data for process similarity measures. To this end, three types of similarity measures, i.e., data item set-based similarity, data operation set-based similarity, and data-aware behavior-based similarity, are introduced to quantify the similarity of data-aware business processes from different perspectives. By comparative evaluation, the effectiveness and applicability are demonstrated. The proposed methodology is readily applicable to enterprise process repository management problems. [ABSTRACT FROM AUTHOR]